Abstract
Pervasive applications, such as location-based services and natural habitat monitoring, have attracted plenty of research interest. These applications make use of a large number of remote positioning devices like Global Positioning System (GPS) for collecting users’ physical locations. Generally, these devices have battery power limitation. They also cannot report very accurate position values. In this paper, we consider the evaluation of a long-standing (or continuous) query over inaccurate location data collected from positioning devices. Our goal is to develop an energy-efficient protocol, which provides some degree of confidence on the query answers evaluated on imperfect data. In particular, we propose the probabilistic filter, which governs GPS devices to decide upon whether location values collected should be reported to the server. We further discuss how these filters can be developed. This scheme reduces the cost of transmitting location updates, and hence the energy spent by the GPS devices. It also allows some portion of query processing to be deployed to the devices, thereby alleviating the processing burden of the server.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Cheng, R., Kalashnikov, D.V., Prabhakar, S.: Evaluating probabilistic queries over imprecise data. In: SIGMOD 2003, pp. 551–562 (2003)
Cheng, R., Kalashnikov, D.V., Prabhakar, S.: Querying imprecise data in moving object environments. IEEE Trans. on Knowl. and Data Eng. 16(9), 1112–1127 (2004)
Cheng, R., Kao, B., Prabhakar, S., Kwan, A., Tu, Y.-C.: Adaptive stream filters for entity-based queries with non-value tolerance. In: VLDB 2005, pp. 37–48 (2005)
Cheng, R., Xia, Y., Prabhakar, S., Shah, R., Vitter, J.S.: Efficient indexing methods for probabilistic threshold queries over uncertain data. In: VLDB 2004, pp. 876–887 (2004)
Dalvi, N.N., Suciu, D.: Efficient query evaluation on probabilistic databases. In: VLDB 2004, pp. 864–875 (2004)
Gedik, B., Liu, L.: Mobieyes: Distributed processing of continuously moving queries on moving objects in a mobile system. In: Bertino, E., Christodoulakis, S., Plexousakis, D., Christophides, V., Koubarakis, M., Böhm, K., Ferrari, E. (eds.) EDBT 2004. LNCS, vol. 2992, pp. 67–87. Springer, Heidelberg (2004)
Gedik, B., Wu, K.-L., Yu, P.S.: Efficient construction of compact shedding filters for data stream processing. In: ICDE 2008, pp. 396–405 (2008)
Han, S., Chan, E., Cheng, R., Lam, K.-Y.: A statistics-based sensor selection scheme for continuous probabilistic queries in sensor networks. Real-Time Syst. 35(1), 33–58 (2007)
Hsueh, Y.-L., Zimmermann, R., Ku, W.-S.: Adaptive safe regions for continuous spatial queries over moving objects. In: Zhou, X., Yokota, H., Deng, K., Liu, Q. (eds.) DASFAA 2009. LNCS, vol. 5463, pp. 71–76. Springer, Heidelberg (2009)
Leonhardi, A., Rothermel, K.: Architecture of a large-scale location service. In: ICDCS 2002, p. 465. IEEE Computer Society, Washington (2002)
Ljosa, V., Singh, A.K.: Apla: Indexing arbitrary probability distributions. In: ICDE 2007, pp. 946–955 (2007)
Olston, C., Jiang, J., Widom, J.: Adaptive filters for continuous queries over distributed data streams. In: SIGMOD 2003, pp. 563–574. ACM, New York (2003)
Pfoser, D., Jensen, C.S.: Capturing the uncertainty of moving-object representations. In: Proceedings of the 6th International Symposium on Advances in Spatial Databases, pp. 111–132. Springer, London (1999)
Prabhakar, S., Xia, Y., Kalashnikov, D.V., Aref, W.G., Hambrusch, S.E.: Query indexing and velocity constrained indexing: Scalable techniques for continuous queries on moving objects. IEEE Trans. Comput. 51(10), 1124–1140 (2002)
Silberstein, A., Munagala, K., Yang, J.: Energy-efficient monitoring of extreme values in sensor networks. In: SIGMOD 2006, pp. 169–180. ACM, New York (2006)
Sistla, P.A., Wolfson, O., Chamberlain, S., Dao, S.: Querying the uncertain position of moving objects. In: Temporal Databases: Research and Practice, pp. 310–337. Springer, Heidelberg (1998)
Tao, Y., Cheng, R., Xiao, X., Ngai, W.K., Kao, B., Prabhakar, S.: Indexing multi-dimensional uncertain data with arbitrary probability density functions. In: VLDB 2005, pp. 922–933. VLDB Endowment (2005)
Wolfson, O., Sistla, A.P., Chamberlain, S., Yesha, Y.: Updating and querying databases that track mobile units. Distrib. Parallel Databases 7(3), 257–387 (1999)
Xiong, X., Mokbel, M.F., Aref, W.G.: Sea-cnn: Scalable processing of continuous k-nearest neighbor queries in spatio-temporal databases. In: ICDE 2005, pp. 643–654. IEEE Computer Society, Washington (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chen, J., Cheng, R., Zhang, Y., Jin, J. (2009). A Probabilistic Filter Protocol for Continuous Queries. In: Rothermel, K., Fritsch, D., Blochinger, W., Dürr, F. (eds) Quality of Context. QuaCon 2009. Lecture Notes in Computer Science, vol 5786. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04559-2_8
Download citation
DOI: https://doi.org/10.1007/978-3-642-04559-2_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04558-5
Online ISBN: 978-3-642-04559-2
eBook Packages: Computer ScienceComputer Science (R0)